Will there be some promising techniques that can make AI greener and affordable in the future?
As far as I know, green AI and red AI are very recent terms and/or research areas, but their importance will or should definitely be more highlighted in the coming years (for obvious reasons).
I don't know how many people are actively and directly researching this topic, but, in the past, I've already read a research paper on the topic, so a few people are already trying to at least raise awareness about these issues. Moreover, they also suggest that researchers report the number of floating-point operations (FLOPs) performed during the experiments, in addition to the usual performance metrics (such as the accuracy). I don't remember all the details of this paper, but I really recommend that you read it. This another possibly useful paper that people interested in this topic might want to read.
Although I'm currently not doing research on this topic, from an algorithmic perspective, improvements in the following areas will definitely contribute to greener AI

zero-, one- or few-shot learning
transfer learning
sample efficiency (in reinforcement learning)

Why? Because learning from fewer samples typically means fewer computations, so fewer emissions.
So, I think that making AI greener is definitely possible, as it's possible to use an electric car rather than a petrol/diesel car, although more research on the topics mentioned above (and other topics) needs to be done to make it practically useful.